A typical chromatographic analysis system includes an injection port into which the sample is injected and mixed with an inert gas or liquid, a column through which the various dissolved components of the sample will travel at a rate related to the characteristics of the specific components, and a detector for measuring the concentration of each component as it exits the column and generating a time-varying signal corresponding to its concentration. The time between the injection of a sample and the detection of a specific component is called the retention time of that component. The time-varying signal is sent to a computing apparatus that integrates the signal by executing a computer program. The computing apparatus often provides a two-dimensional visual display of the signal, known in the art as a chromatogram. The x-axis of the chromatogram represents time, and the y-axis typically corresponds to the amplitude of the signal.
A typical time-varying signal 101 generated by a gas chromatograph is shown in FIG. 1. Peaks 102 in the signal correspond to the detection by the chromatograph of specific components of the chemical sample. The height of the peak above the background level 103 of the signal, and the amount of area under the peak and above the background level, correlate to the amount of the respective component present in the sample. This information can be used for many applications, such as to check for the presence of, or to ensure the absence of, particular components in the sample, or to verify the proper concentration of the component in the sample.
The computer program includes an integration method which detects peaks, determines the retention time 104 at which they occur, and determines their heights and areas. To determine peak height and peak area, the computer must first determine a baseline for each peak. The baseline is a reference level that corresponds to the background level of the signal when no peaks are present. The background level is typically a non-zero signal that may include noise (higher in frequency than the peaks) and drift (lower in frequency than the peaks; also known as wander).
A chromatographer visually drawing the baseline under a particular peak would extrapolate it through the center of the noise on both sides of the peak with a slope corresponding to the drift. However, the presence of noise and drift in the background level make determining an accurate baseline difficult for a computer. As a result, prior art integration methods, in the presence of noise and drift, typically do not produce the same baseline as would a chromatographer looking at the signal visually.
A first prior art integration method sets the baseline by looking at only a small region of the signal at a time. As shown in FIG. 2, this method sets the baseline 201 too low where noise 202 is present because the method finds the lowest points near both sides of the peak and draws the baseline 201 between these points. If, as shown in FIG. 3, the detector produces a negative background signal disturbance 302 in the presence of two large peaks close together, this integration method will use the lowest point of the disturbance to set the baseline 301. Also, since the threshold level for signal detection has to be set conservatively in order to avoid misidentifying background noise as peaks, some valid peaks may not be detected. This problem worsens as the signal-to-noise ratio decreases, as shown by the absence of any identified peaks for the signal 401 of FIG. 4.
A second prior art method extends the baseline back in time from the average background level that occurs towards the end of an analysis in order to compensate for the effects of venting the solvent peak. As shown in FIG. 5, this method sets the baseline 501 too low in the region where background drift 504 occurs after the venting of the solvent peak 502. In addition, if the background continues to drift throughout the run, this method may misinterpret a hump in the drift as a false peak 505.
A third prior art method establishes a baseline using curve fitting techniques. However, the complexity of curve fitting increases with the complexity of the baseline shape, which makes such a method difficult to implement and requires excessive computational power when the signal contains noise and drift. To reduce complexity, this method may alternatively divide the baseline into multiple segments to allow simpler, piecewise curve fitting. However, this modification introduces discontinuities between curve segments that affect the accuracy of integration results.
A fourth prior art method filters noise by performing a fourier transform. However, it is difficult to filter out all the noise without distorting the peaks, since typically there is overlap in frequency content between the peaks and the noise.
A chromatogram which displays an accurate baseline along with the signal is readily usable by an expert chromatographer. However, chromatograms are sometimes analyzed by nonchromatographers, for whom baseline drift and noise can cause confusion. A better display for non-experts would present a chromatogram from which the baseline noise and drift have been filtered, leaving only undistorted peaks and a flattened baseline. A filtered chromatogram could be accurately integrated using simple integration methods, and would provide more accurate results when analyzed using simulated distillation techniques.
In the analysis of signals consisting of one or more peaks rising above a background level which contains noise, drift, or a combination of both, there has been a need for a simple method of determining an accurate baseline which eliminates noise and drift without distorting the peaks, in order to calculate accurate peak heights and areas. There has also been a corresponding need for filtering the signal to eliminate noise, drift, or both, prior to display, integration, or simulated distillation.